Welcome to the PeakPerformance documentation!

PeakPerformance is a Python toolbox for Bayesian inference of peak areas.

It defines PyMC models describing the intensity curves of chromatographic peaks.

Using Bayesian inference, this enables the fitting of peaks, yielding uncertainty estimates for retention times, peak height, area and much more.

This documentation features various notebooks that demonstrate the usage.

Installation

It is highly recommended to follow the following steps and install PeakPerformance in a fresh Python environment:

1. Install the package manager Mamba. Choose the latest installer at the top of the page, click on “show all assets”, and download an installer denominated by “Mambaforge-version number-name of your OS.exe”, so e.g. “Mambaforge-23.3.1-1-Windows-x86_64.exe” for a Windows 64 bit operating system. Then, execute the installer to install mamba and activate the option “Add Mambaforge to my PATH environment variable”.

Caution

If you have already installed Miniconda, you can install Mamba on top of it but there are compatibility issues with Anaconda.

Note

The newest conda version should also work, just replace mamba with conda in step 2.)

2. Create a new Python environment in the command line using the provided environment.yml file from the repo. Download environment.yml first, then navigate to its location on the command line interface and run the following command:

mamba env create -f environment.yml

Naturally, it is alternatively possible to just install PeakPerformance via pip:

pip install peak-performance

You can also download the latest version from GitHub.

Tutorials

• Preparing raw data
• Composition and assumptions of peak models
• PeakPerformance workflow
• Validation of PeakPerformance
• Diagnostic plots
• How to adapt PeakPerformance to your methods / data

Examples

• Example 1: Build a pipeline using PeakPerformance’s convenience functions
• Example 2: Directly access PeakPerformance’s functions to create a custom pipeline
• Example 3: Example with a larger experimental data set

In the following case studies we investigate certain aspects of peak modeling.

Case Studies

• Investigation of double-peak separation
• Investigating the effect of noise on the area uncertainty quantification

Below you can find documentation that was automatically generated from docstrings.

API Reference

• peak_performance.models
• peak_performance.pipeline
• peak_performance.plots